E02 - Intro to R (Analysis of Gene Expression @ VSCHT)

Copy, please, these files and directories to your personal directory:

cp -r /data/shared/AGE2021/Exercises/E02-intro_to_R ~/AGE2021/Exercises

RMarkdown (Rmd)

We will be using RMarkdown for exercises. It allows to combine Markdown and (not only) R code to generate documents in HTML, PDF, Word (docx) and many more formats. This is how it works:

Code chunks in RMarkdown document are evaluated by knitr and converted to Markdown, which is then converted by pandoc to selected output format (in our case HTML).

Useful links

How to use RMarkdown

Pandoc header

On the beginning of the source file you can see the header section separated by ---. Those are options for pandoc, mostly specifying how output (e.g. HTML) should look like.

Markdown syntax

Markdown is an easy-to-write/easy-to-read markup language. It is widely used by, but not only, developers for documentation (you have certainly seen it on GitHub). Markdown syntax is super easy and so, please, refer to Markdown cheatsheet (however, pandoc uses an extended version of Markdown).

Using HTML tags

Besides Markdown syntax, you can use anything you would normally use in HTML. For example, <div style="color: red;">Some red text.</div>

Some red text.

Just be careful, because RMarkdown is not a classic HTML document and you don’t have a full control over conversion to output HTML.

Code chunks

Code (R, Python, etc.) is placed in chunks. A chunk begins with triple backticks followed by {language_name} and ends with another triple backticks. Both start and end triple backticks must be on separate lines.

Example of chunk with R code:

```{r}
df <- data.frame()
```

Output from a chunk is generated below the chunk.

Each chunk can have options set. For example,

```{r, warning = FALSE}
df <- data.frame()
```

will suppress all warnings produced by this chunk. See knitr options.

You can set these options globally via knitr package:

library(knitr)
opts_chunk$set(warning = FALSE)

And override them in individual chunks.

JavaScript and CSS are also supported. Code in chunks of type js and css will be put to <script> and <style> tags inside <head>.

Inline code

You can also place R code inline using `r code`. Inline code in final document will be replaced by its output.

Rendering a document

On the end of this document you will find a chunk, which generates a HTML document from this source. If you are curious how an intermediate Markdown document looks like, you can specify clean = FALSE in render() function and then inspect the intro_to_R.md file.

Some RMarkdown tips

Pretty tables

We can use DT package, an R interface to JavaScript library datatables.

DT::datatable(
  mtcars[, 1:5],
  filter = "top",
  class = "display",
  rownames = TRUE
)

Or kable and kableExtra for simplier tables.

kableExtra::kable_styling(kableExtra::kbl(head(mtcars)), bootstrap_options = c("striped", "hover", "condensed"))

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.460	20.22	1	0	3	1

Using tab design

As you can see, this document has autogenerated menu on the left. However, pandoc also allows to use a tab-style menu. See this (HTML) example.

Personally, I have found this design very useful for reporting.

Generating Markdown and HTML from R

The cool feature of code chunks is also the ability of raw output, by setting results = "asis". For example, this chunk produces a Markdown header:

```{r, results = "asis"}
cat("\n\n# Header\n\n")
```

I have found this to be very useful for dynamic reports, e.g. header names are composed from sample groups.

Reproducible R

Well, this could be a chapter on its own, but only a few important tips will be given, basically to avoid things like these:

setwd("C:\Users\jenny\path\that\only\I\have") will obviously work only on the computer of creator.
rm(list = ls()) will not perform the full restart of R, it will just remove user-defined objects. In addition, what if I accidentally run your script and it will remove a week of hard computations on my computer?

Project-oriented workflow

As you know, we have created the RStudio project for AGE exercises, with its top-level directory being ~/AGE2021/Exercises (you can find the Exercises.Rproj file there). Whenever you open the project in RStudio, it will automatically set the working directory to the top-level of the project.

Now that you have this information, you can think of “I will stop using setwd() and always use paths relative to top-level directory”. Unfortunately, some packages are evil and set your working directory silently, which leads to unexpected and hard-to-debug errors (one example is knitr described in the RMarkdown chapter below). Luckily, for a consistent navigation in your project, here package was created.

Let’s see how it works. The main function is here::here():

here()

## [1] "/data/persistent/jirinovo/bio-class-deb10/AGE2021/Exercises"

here("E02-intro_to_R", "images")

## [1] "/data/persistent/jirinovo/bio-class-deb10/AGE2021/Exercises/E02-intro_to_R/images"

It will join path relative to the project’s top-level directory with its absolute path. But what happens if we change the working directory?

# Just for educational purposes!
setwd("E01-intro")
cat("Current working directory:\n")
getwd()

Current working directory:
[1] "/data/persistent/jirinovo/bio-class-deb10/AGE2021/Exercises/E01-intro"

cat("here():\n")
here()

here():
[1] "/data/persistent/jirinovo/bio-class-deb10/AGE2021/Exercises"

cat("Some path:\n")
here("E02-intro_to_R", "images")
setwd(here())

Some path:
[1] "/data/persistent/jirinovo/bio-class-deb10/AGE2021/Exercises/E02-intro_to_R/images"

Hmm, nothing happened after setwd(), right? This way, no hidden setwd() can surprise you 😎

How is it possible here always knows where is the top-level of project located? Well, here is smart: it will scan your project directory structure and do some heuristics. The most basic one is searching for file ending with .Rproj. Wait! Is that one of the reasons why we have created our RStudio project? Yes, exactly 🤓

You can find more information about project-oriented workflow here and about safe here paths here and here.

Do not automatically/reload your R session

Otherwise you can expect the unexpected.

Always start R with a blank state.

Because your are using R through RStudio Server, don’t worry when you close your browser - R session will remain running.

Prevent package namespace conflicts

When you load a package with library(), all exported objects from that packages will be attached to the global namespace, while replacing existing objects. Let’s see an example (restart your R session before). Function named select() is very common, for example it is present in these two packages:

AnnotationDbi::select

## standardGeneric for "select" defined from package "AnnotationDbi"
## 
## function (x, keys, columns, keytype, ...) 
## standardGeneric("select")
## <bytecode: 0x55c1c233f388>
## <environment: 0x55c1c2538dd8>
## Methods may be defined for arguments: x
## Use  showMethods("select")  for currently available ones.

dplyr::select

## function (.data, ...) 
## {
##     UseMethod("select")
## }
## <bytecode: 0x55c1bc751528>
## <environment: namespace:dplyr>

If we use package prefix to locate the select() function (AnnotationDbi:: or dplyr::), no problems can arise.

But see what happens when we load these two packages:

library(AnnotationDbi)
library(dplyr)
select

function (.data, ...) 
{
    UseMethod("select")
}
<bytecode: 0x5558f010cba8>
<environment: namespace:dplyr>

You see that select() from dplyr won, because this package was loaded after AnnotationDbi. This could lead to unexpected results: you think you are using a function from a package, but instead you are using a function with the same name, but from an another package, which was loaded last.

To prevent such confusions, we can use the conflicted package. It is a sort of guardian and dictator in one person: it throws error when you attempt to use a function with ambiguous name, and also allows you to select a preferred package for such conflicts. Let’s see it in action (restart your R session again):

library(conflicted)
library(AnnotationDbi)
library(dplyr)
select()

Error: [conflicted] `select` found in 2 packages.
Either pick the one you want with `::` 
* dplyr::select
* AnnotationDbi::select
Or declare a preference with `conflict_prefer()`
* conflict_prefer("select", "dplyr")
* conflict_prefer("select", "AnnotationDbi")
Run `rlang::last_error()` to see where the error occurred.

You see that select is conflicting and conflicted offers you two options how to resolve it:

Use the package prefix:

dplyr::select

Or use conflict_prefer() to select a preferred package:

conflict_prefer("select", "dplyr")
select

[conflicted] Will prefer dplyr::select over any other package

function (.data, ...) 
{
    UseMethod("select")
}
<bytecode: 0x56350f4713e8>
<environment: namespace:dplyr>

conflicted should be loaded as the first package.

Unfortunately, I encountered some RStudio freezes during the usage of conflicted. In that case refer to the chapter “What to do when RStudio is stuck” in E01-intro.

Use renv to capture your project dependencies

This is really not needed for AGE, but anyway it is a good practice to somehow export all packages needed to run your project, and renv is a great tool for that. It basically does two things:

Creates a separate R library for your project. This way, all packages are installed into this library instead of the system one. So when some project needs a particular package version, you won’t mess up your system library.
It searches your project files for library() and package:: usages and exports packages with exact versions to a file called renv.lock. Based on this file, everyone is able to install all packages needed for a project.

It is somehow similar to conda 🙂

Use targets: a Make-like pipeline toolkit for R

The targets package is a Make-like pipeline toolkit for Statistics and data science in R. With targets, you can maintain a reproducible workflow without repeating yourself. targets skips costly runtime for tasks that are already up to date, runs the necessary computation with implicit parallel computing, and abstracts files as R objects. A fully up-to-date targets pipeline is tangible evidence that the output aligns with the code and data, which substantiates trust in the results.

This is really out of scope of AGE, but targets is the way reproducible R analyses should be done.

Installing R packages

For bioinformatic purposes we will be using packages from Bioconductor, which groups together packages for bioinformatics. There are several core packages developed by Bioconductor’s team which share the same philosophy (API) and are used by other third-party packages. For example, SummarizedExperiment is used for storing experiment data (expression matrix, phenotype data, genomic ranges).

Bioconductor packages are installed through BiocManager::install() function. However, because some Bioconductor packages can interefere with packages from R main repository CRAN, always use BiocManager::install() to install packages, regardless their source repository.

It is also possible to install packages from GitHub repository with BiocManager::install("user/repository"), for example BiocManager::install("tidyverse/stringr"). BiocManager recognizes / as a GitHub source and uses devtools::install_github() underneath.

If you run BiocManager::install(), it will detect outdated packages and ask you whether to update them. However, some of them are system-wide and must be installed from R running with root privileges, see below.

If you install a package from R session launched from user shell (that is the R session in RStudio), it will be installed to your personal library in home directory. However, some packages must be installed to system library (usually the base R packages like Matrix), so you need to run R as the root (sudo R) and then install/update packages.

Debugging R code

Because R has dynamic typing and its error tracing is not as good as in e.g. Python, its debugging could be very painful. Here are some tips for saving your mental health (or refer to this):

`traceback()`

When error occurs, its message is usually not very informative. traceback() function will print a call stack of last error, helping you to resolve a location of the error.

Let’s define the group of functions, which call each other, starting from f():

f <- function(x) g(x)
g <- function(y) h(y)
h <- function(z) {
  z**2
}

f(2)

## [1] 4

f("2")

## Error in z^2: non-numeric argument to binary operator

Let’s inspect the call stack:

traceback()

## 2: match.fun(FUN)
## 1: lapply(as.matrix(mtcars))

Also try running f("a") in your prompt. RStudio will directly offer you to show traceback or rerun code with debug (see below).

Interactive debugger

Sometimes you need more information from a location of error. Interactive debugger allows you to pause execution of a function and interactively explore its state. It is invoked by calling the browser() function:

f2 <- function(x) g2(x)
g2 <- function(y) h2(y)
h2 <- function(z) {
  browser()
  z**2
}

f2("2")

Interactive debugger can be also invoked by RStudio, as is shown above in the traceback() section.

Unfortunately, browser() is not working well from within code chunks, so rather run debugged function from the R command line (interactive session).

`recover()`

Another way to activate browser() is to use options(error = recover). Now when you get an error, you will get an interactive prompt that displays the traceback and gives you the ability to interactively debug inside any of the frames:

options(error = recover)
f("2")

To restore the default on-error behaviour, use dump.frames:

options(error = dump.frames)

Try to utilize vectorized operations

R’s fundamental feature is vectorization. All basic data types (numeric, character, etc.) are vectors and most of base functions are vectorized. For example, this is unnecessary in R:

my_vector <- c(1, 2, 3, 4, 5)
for (i in 1:length(my_vector)) {
  my_vector[i] <- my_vector[i] + 1
}
my_vector

## [1] 2 3 4 5 6

Instead you should use the addition operation, which is vectorized:

my_vector + 1

## [1] 3 4 5 6 7

Try to avoid for-loops whenever possible, because:

They have side effects:
- Leaving iterating variable in environment. From the example above, the variable i has value 5.
- They could change other variables from from within a loop.
They could be also slow. Those are mostly cases when in each loop you add something new to a vector or matrix, which, then, needs to be re-sized, causing a re-allocation of memory. Two great posts on this topic are here and here (chapter 4).

So, what to use in R instead of for-loops? Besides vectorized functions there are several apply-family functions. They take data (vector, list, dataframe, …) as an input and apply a function on each of its element. Let’s introduce the most basic ones:

sapply() apply a function to each element of vector or list and returns a vector:

my_vector <- 1:5
sapply(my_vector, function(x) {
  x ** 2
})

## [1]  1  4  9 16 25

lapply() is similar, but returns a list instead of vector. It can be also used for dataframes, where a function is applied to each of dataframe’s column (dataframe is basically a list of column-lists):

lapply(my_vector, function(x) {
  x ** 2
})

## [[1]]
## [1] 1
## 
## [[2]]
## [1] 4
## 
## [[3]]
## [1] 9
## 
## [[4]]
## [1] 16
## 
## [[5]]
## [1] 25

lapply(mtcars, head)

## $mpg
## [1] 21.0 21.0 22.8 21.4 18.7 18.1
## 
## $cyl
## [1] 6 6 4 6 8 6
## 
## $disp
## [1] 160 160 108 258 360 225
## 
## $hp
## [1] 110 110  93 110 175 105
## 
## $drat
## [1] 3.90 3.90 3.85 3.08 3.15 2.76
## 
## $wt
## [1] 2.620 2.875 2.320 3.215 3.440 3.460
## 
## $qsec
## [1] 16.46 17.02 18.61 19.44 17.02 20.22
## 
## $vs
## [1] 0 0 1 1 0 1
## 
## $am
## [1] 1 1 1 0 0 0
## 
## $gear
## [1] 4 4 4 3 3 3
## 
## $carb
## [1] 4 4 1 1 2 1

apply() takes as an input dataframe or matrix and applies a function on either rows or columns, returning a single value for each of them. Row-apply and column-apply are specified by the MARGIN parameter: 1 for rows and 2 for columns.

apply(mtcars, MARGIN = 1, mean)

##           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
##            29.90727            29.98136            23.59818            38.73955            53.66455            35.04909            59.72000            24.63455 
##            Merc 230            Merc 280           Merc 280C          Merc 450SE          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
##            27.23364            31.86000            31.78727            46.43091            46.50000            46.35000            66.23273            66.05855 
##   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
##            65.97227            19.44091            17.74227            18.81409            24.88864            47.24091            46.00773            58.75273 
##    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
##            57.37955            18.92864            24.77909            24.88027            60.97182            34.50818            63.15545            26.26273

apply(mtcars, MARGIN = 1, mean)

##           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
##            29.90727            29.98136            23.59818            38.73955            53.66455            35.04909            59.72000            24.63455 
##            Merc 230            Merc 280           Merc 280C          Merc 450SE          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
##            27.23364            31.86000            31.78727            46.43091            46.50000            46.35000            66.23273            66.05855 
##   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
##            65.97227            19.44091            17.74227            18.81409            24.88864            47.24091            46.00773            58.75273 
##    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
##            57.37955            18.92864            24.77909            24.88027            60.97182            34.50818            63.15545            26.26273

Introduction to tidyverse

Until now you have been probably using only the base R. Tidyverse can be seen as a dialect of R and is described as follows:

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.

Tidyverse is very popular these days, but as some (here and here) have noted, it is not suitable for R beginners, meaning you should first know how base R is working and only then learn tidyverse.

Useful links

List of tidyverse packages
R for Data Science - an amazing tutorial book for R which is mainly using tidyverse.
Awesome list of cheatsheets. Some of them are also accessible in RStudio: Help -> Cheatsheets

Tidy (long) data

Tidyverse packages are not meant to replace the base R. Especially, they are not suited for matrix manipulation, and are specifically used for long data (also called tidy data). More about tidy data can be found here.

The following three rules make a dataset tidy:

Each variable must have its own column.
Each observation must have its own row.
Each value must have its own cell.

Following three rules makes a dataset tidy: variables are in columns, observations are in rows, and values are in cells.

As you will see, we will be mostly working with biological matrices (= data in wide format). For example, consider this datafame:

(df_wide <- data.frame(sample_1 = 1:2, sample_2 = 3:4, sample_3 = 5:6, row.names = c("gene_A", "gene_B")))

For each sample (column) and each gene (row) we have its value (cell). This form of data is usable for functions operating on matrices (e.g. PCA), but as you will see later, not for e.g. plotting (besides base R plotting).

This dataframe has wide format, and thus it is not tidy. You can see that rownames and colnames are, in fact, values of variables Gene and Sample, respectively. So how would this dataframe look like in tidy format? Don’t be surprised now, we will get to this code later.

library(magrittr)

df_long <- df_wide %>%
  tibble::rownames_to_column("Gene") %>%
  tidyr::pivot_longer(-Gene, names_to = "Sample", values_to = "Count")
df_long

Now df_long is tidy and corresponds to long format of the original dataframe. We actually did this:

Converting wide data to long format.

Why is long data useful for some purposes (mainly for plotting)? Well, for biological matrix like the one in df, you usually also have a sample sheet, for example:

(sample_sheet <- data.frame(Sample = c("sample_1", "sample_2", "sample_3"), Age = 1:3, Treatment = c("yes", "no", "yes")))

Would it be neat to have this information alongside the long data (df_long)? I think so:

dplyr::left_join(df_long, sample_sheet, by = "Sample")

As you will see later, this long form of data is extremely useful for plotting. In this blog post, there is a nice example of using tidy data and tidyverse in genomics.

Non-standard evaluation (NSE)

Let’s go ahead a bit and look at this select() function from dplyr package (will be introduced later):

dplyr::select(mtcars, cyl, mpg) %>% head()

You can see that select() is filtering columns of the dataframe. But it has a strange syntax, hasn’t it? We have used variables, which are not defined. Don’t be scared by this dark magic - it is based on R’s lazy evaluation (details) and it’s called non-standard evaluation (NSE). In short, R only evaluates the expression if the expression is actually used.

Tidyverse functions use NSE to look first at variables inside a passed object (columns = variables). Most of these functions are designed to have their first parameter as data object. When variables are not found inside the object, function will look for variables in the global environment (this can cause unwanted problems, when object has variable with the same name as the one in global environment). So in the example above, cyl and mpg variables are evaluated in the context of mtcars object and environment defined in select() function call.

NSE is a quite advanced topic and its knowledge is mainly needed for implementation of custom functions, which share the tidyverse philosophy. See vignette("programming") and friendlyeval, a friendly interface for NSE programming.

Now we will quickly look at the most popular tidyverse packages.

`magrittr` - pipe operator

The pipe operator is probably the basic compound of shell programming. Fortunately for us, it is also implemented in R.

The pipe operator (see ?`%>%`) is forwarding output from left hand side (LHS) to input on right hand side (RHS). The default behaviour is to pipe output to the first argument of a RHS function.

So instead of using nested functions like

head(rownames(mtcars))

## [1] "Mazda RX4"         "Mazda RX4 Wag"     "Datsun 710"        "Hornet 4 Drive"    "Hornet Sportabout" "Valiant"

you can use the pipe operator:

rownames(mtcars) %>% head()

## [1] "Mazda RX4"         "Mazda RX4 Wag"     "Datsun 710"        "Hornet 4 Drive"    "Hornet Sportabout" "Valiant"

And there are even more types of pipes.

Piped object is available on RHS as the . object. It is used when you want:

To use an argument different than first of the RHS function:

c("first", "second", "third") %>% paste("I am", ., sep = " ")

## [1] "I am first"  "I am second" "I am third"

To use the LHS output as a regular R object:

mtcars %>% .$cyl

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

mtcars %>% .[1:5, 1:3]

To use a lambda expression. They behave like a function with single parameter .:

mtcars %>% {head(.$cyl)}

## [1] 6 6 4 6 8 6

mtcars %>% {
  my_data <- .
  c(min(my_data), max(my_data))
}

## [1]   0 472

Or you can define an anonymous function:

mtcars %>% (function(my_data) c(min(my_data), max(my_data)))

## [1]   0 472

Using arithmetic and other operators together with pipe can be more pleasant with provided aliases.

Add and subtract operators are working fine:

1 %>% + 2 %>% - 3

## [1] 0

But for the sake of consistency, rather use operator aliases:

1 %>% add(2) %>% subtract(3)

## [1] 0

However, how would you use, for example, == or * operator?

1 %>% == 2
1 %>% * 3

## Error: <text>:1:7: unexpected '=='
## 1: 1 %>% ==
##           ^

You have to use aliases:

1 %>% equals(2)

## [1] FALSE

1 %>% multiply_by(3)

## [1] 3

In fact, all aliases are actually shorthands for writing operators as “classic” functions, e.g. `*`(x, y). So the examples above can be also written as:

1 %>% `==`(2)

## [1] FALSE

1 %>% `*`(3)

## [1] 3

`tibble` - enhanced data.frame

There are some differences to data.frame (see link above and examples here), but the most visible difference is in printing of a tibble:

tibble::as_tibble(mtcars)

tibble is showing the data types of variables. Also you can see that there aren’t rownames - this is strange, but it is one of the features of tibble. Authors of tibble package are convinced that rownames should be represented by variable (column) and not by object’s attribute, accessible by rownames() method.

This is bad particularly for usage of tidyverse together with Bioconductor, where rownames are an essential component. Many tidyverse functions return a tibble or drop rownames, but most of times we rather want data.frame with defined rownames. tibble can be converted to data.frame by as.data.frame() function.

To preserve rownames in a new column, use the tibble::rownames_to_column("name_of_new_column") function.

Useful is the tribble() function for more readable creation of (small) tables in code:

tibble::tribble(
  ~column_A, ~column_B,
  "a",   1,
  "b",   2,
  "c",   3
)

`dplyr` - data manipulation

dplyr is probably the most popular package from tidyverse. It helps to solve the most common data manipulation challenges and its sibling dbplyr also supports remote data stored in a database (MySQL, PostreSQL, SQLite, and others).

Some functions in dplyr have names similar to functions from other packages. Thus, it is recommended to always specify the dplyr:: namespace.

Cheatsheet

`select()` - select (filter) columns

library(dplyr)
mtcars_sm <- head(mtcars)
dplyr::select(mtcars_sm, cyl, mpg)

By using - you can also specifify columns to omit:

dplyr::select(mtcars_sm, -cyl, -mpg)

We can select a range of variables. Numerical range is also supported, e.g. 1:5. Omitting a range is also possible - in this example you would use -(disp:drat).

dplyr::select(mtcars_sm, disp:drat)

There are also very useful select helpers. For omitting they can be also combined with -.

Select columns starting with “c”:

dplyr::select(mtcars_sm, starts_with("c"))

Select columns containing “ar”:

dplyr::select(mtcars_sm, contains("ar"))

Select column at last - 1 position:

dplyr::select(mtcars_sm, last_col(1))

Select columns having names in vector and don’t throw error on non-existing column:

dplyr::select(mtcars_sm, one_of(c("mpg", "cyl", "garbage")))

Because this will fail:

dplyr::select(mtcars_sm, mpg, cyl, garbage)

## Error: Can't subset columns that don't exist.
## x Column `garbage` doesn't exist.

select() can also select and rename columns at the same time:

dplyr::select(mtcars_sm, cylinders = cyl, horse_power = hp)

To only rename specific and keep other columns, use rename() function:

dplyr::rename(mtcars_sm, cylinders = cyl, horse_power = hp)

Using the everything() select helper also works, but you will lose the original column order:

dplyr::select(mtcars_sm, cylinders = cyl, horse_power = hp, everything())

select() always returns an object of the same type as input. However, sometimes you rather need a vector: pull() returns a vector of specified variable.

dplyr::pull(mtcars_sm, cyl)

## [1] 6 6 4 6 8 6

`arrange()` - order rows

arrange() orders rows by specified variables.

dplyr::arrange(mtcars_sm, cyl, disp)

Use desc() or - for descending order:

dplyr::arrange(mtcars_sm, desc(cyl))

# or
# dplyr::arrange(mtcars_sm, -cyl)

`filter()` - filter rows

In fact, filter() is very similar to base R function subset(), but offers much more functionality.

Multiple arguments are equivalent to AND operator:

dplyr::filter(mtcars_sm, hp > 100, cyl == 6)

So this is equivalent:

dplyr::filter(mtcars_sm, hp > 100 & cyl == 6)

Thanks to NSE we can do some magic. Here, we want rows where hp is greater than mean value of hp minus 10:

dplyr::filter(mtcars_sm, hp > mean(hp) - 10)

`mutate()` - add new or modify existing variables

Named parameters of mutate() function create new variables (or modify existing, if a parameter name is same).

tibble::rownames_to_column(mtcars_sm, "car_name") %>%
  dplyr::mutate(cyl2 = cyl * 2) %>%
  dplyr::select(car_name, cyl, cyl2)

mutate() is one of the tidyverse functions, which silently drop rownames. To preserve them, we have moved rownames to the new column car_name.

This is how we remove the column and modify the existing one:

dplyr::mutate(mtcars_sm, mpg = NULL, cyl = cyl * 2)

Very useful are these “if” functions:

if_else() checks for a condition and returns values for TRUE and FALSE matches. In missing parameter you can also specify a return value for NA values. if_else() is similar to base R ifelse(), but checks for the same return types.

dplyr::mutate(mtcars_sm, car_category = if_else(hp >= 100, "strong", "weak")) %>%
  dplyr::select(hp, car_category)

You can see that return values must be the same type:

dplyr::mutate(mtcars_sm, car_category = if_else(hp >= 100, "strong", 50)) %>%
  dplyr::select(hp, car_category)

## Error: Problem with `mutate()` input `car_category`.
## x `false` must be a character vector, not a double vector.
## ℹ Input `car_category` is `if_else(hp >= 100, "strong", 50)`.

recode() replaces values, similar to switch(). You can specify default return value for not-specified and NA values (.default and .missing parameters). Very useful for replacing factor values while preserving their order.

dplyr::mutate(mtcars_sm, am_char = recode(am, `0` = "zero", `1` = "one")) %>%
  dplyr::select(am, am_char)

recode_factor() create factors with levels ordered as they appear in the recode call.

factor(mtcars_sm$am) %>%
  levels()

## [1] "0" "1"

# You can notice that mutate() parameters are "executed" from first to last.
# So to "am_char" is already passed "am" as factor, as "am" is mutated before "am_char".
dplyr::mutate(
  mtcars_sm,
  am = factor(am),
  am_char = recode_factor(am, `1` = "one", `0` = "zero")
) %>%
  dplyr::pull(am_char) %>%
  levels()

## [1] "one"  "zero"

case_when() allows to specify multiple if statements. Statements are given in a format condition ~ return_value and they are matched from first to last.

For else statement put TRUE ~ some_value as the last statement (if not specified, some_value will be NA). As case_when() is checking for return types, you have to use appropriate NA type: NA_integer_, NA_real_, NA_complex_ or NA_character_. Be careful since 1 is a real number (numeric type), whereas 1L is a true integer.

dplyr::mutate(
  mtcars,
  car_category = case_when(
    hp < 80 ~ "weak",
    hp >= 80 & hp <= 100 ~ "middle",
    hp > 100 ~ "strong"
  )
) %>%
  dplyr::select(hp, car_category)

All these “if” functions are vectorised and can be also used for vectors:

v <- c(1, 1, 2, 3, 10)
if_else(v < 2, "< 2", ">= 2")

## [1] "< 2"  "< 2"  ">= 2" ">= 2" ">= 2"

`group_by()` and `summarise()`

group_by() is similar to SQL operation of the same name. It takes an existing dataframe and converts it into a grouped dataframe where operations are performed “by group”.

(by_cyl <- dplyr::group_by(mtcars, cyl))

Better is to see the original tibble in your console, where is information about groups.

Now we can perform operations on groups. summarise() reduces multiple values down to a single value. Let’s calculate some statistics in the cyl groups:

dplyr::summarise(
  by_cyl,
  mpg_mean = mean(mpg),
  hp_max = max(hp),
  qsec_median = median(qsec),
  wt_sd = sd(wt)
)

n() is a special function to get counts inside groups:

dplyr::summarise(
  by_cyl,
  n = n()
)

filter() is now acting on groups. Let’s compare these two filter operations:

dplyr::filter(mtcars, mpg > mean(mpg))

dplyr::filter(by_cyl, mpg > mean(mpg))

The former keeps rows with mpg greater than the global average whereas the latter keeps rows with mpg greater than the average inside the cyl groups.

To remove grouping, use the ungroup() function.

`*_join()` and `bind_rows()` - joining dataframes

A collection of *_join() functions acts in the same way as in SQL - based on common keys, they join two dataframes together.

mtcars_prices <- tibble::tribble(
  ~cyl, ~price,
  4,    1000,
  6,    5000,
  8,    10000
)

dplyr::left_join(mtcars_sm, mtcars_prices, by = "cyl") %>%
  dplyr::select(cyl, price)

bind_rows() append rows to dataframe. It puts NAs to columns not present in both dataframes.

dplyr::bind_rows(mtcars_sm, mtcars_prices) %>%
  dplyr::select(cyl, price, everything())

`tidyr` - tools for tidy data

The goal of tidyr is to help you to create tidy data. Just to remind you, tidy data is data where:

Every column is variable.
Every row is an observation.
Every cell is a single value.

Cheatsheet on reading and tidying data.

Let’s look at some of basic tasks tidyr can do:

Pivotting

Pivotting converts between long and wide forms. We will reuse the wide df_wide dataframe.

pivot_longer() “lengthens” data, increasing the number of rows and decreasing the number of columns.

df_wide

df_long <- df_wide %>%
  tibble::rownames_to_column("Gene") %>%
  tidyr::pivot_longer(-Gene, names_to = "Sample", values_to = "Count")
df_long

We have added rownames of df_wide to the new column Gene. In pivot_longer() we specified by -Gene we want to lengthen this column.

Of course more than one column can be lengthened. Let’s add more columns to df_wide:

df_wide2 <- df_wide %>%
  tibble::rownames_to_column("Gene_symbol") %>%
  dplyr::mutate(
    Gene_ID = c("ENSG0001", "ENSG0002"),
    Gene_class = c("protein_coding", "miRNA")
  )

df_wide2

df_long2 <- df_wide2 %>%
  tidyr::pivot_longer(-c(Gene_symbol, Gene_ID, Gene_class), names_to = "Sample", values_to = "Count")

df_long2

We can see gene columns have the same prefix, and so we can utilize that: select helpers are allowed here.

df_wide2 %>%
  tidyr::pivot_longer(-starts_with("Gene_"), names_to = "Sample", values_to = "Count")

pivot_wider() “widens” data, increasing the number of columns and decreasing the number of rows.

tidyr::pivot_wider(df_long, names_from = "Sample", values_from = "Count")

tidyr::pivot_wider(df_long2, names_from = "Sample", values_from = "Count")

Splitting and combining character columns

separate() turns a single character column into multiple columns.

(df <- data.frame(x = c(NA, "a.b", "a.d", "b.c")))

tidyr::separate(df, x, c("A", "B"), sep = "\\.")

extract() turns each capturing group into a new column.

(df <- data.frame(x = c(NA, "a-1-x", "a-2-y", "a-3")))

tidyr::extract(df, x, c("A", "B", "C"), regex = "([a-z])-(\\d)-?([a-z])?")

unite() pastes together multiple columns into one.

(df <- data.frame(x = c(NA, "a", "b", "c"), y = c("a", "f", "g", "h")))

tidyr::unite(df, "x_y", x, y, sep = "_", remove = FALSE)

Other `tidyr` functions

drop_na() drops rows containing missing values.

(df <- data.frame(x = c(1, NA, 2), y = c(1, 2, NA)))

tidyr::drop_na(df)

tidyr::drop_na(df, x)

replace_na() replaces missing values.

For dataframes, specify a list of columns and replacements.

tidyr::replace_na(df, list(x = 5, y = 10))

For vectors, specify a replacement value.

tidyr::replace_na(c(1, NA, 3), 2)

## [1] 1 2 3

`stringr` - consistent wrappers for common string operations

stringr is a replacement for base R collection of string manipulation functions (e.g. grep(), gsub(), etc., see ?grep). In my opinion, stringr API is more user-friendly. All its functions start with str_ prefix and are vectorised over input string and pattern.

Useful links

Cheatsheet
RegExplain - RStudio addin providing a friendly interface for working with regular expressions and functions from stringr.
Regex101 - cool web application for regex testing.

Because stringr homepage offers a nice collection of examples, I will just copy-paste them here and possibly add more information.

library(stringr)

x <- c("why", "video", "cross", "extra", "deal", "authority")
str_length(x)

## [1] 3 5 5 5 4 9

str_c(..., sep = "", collapse = NULL) concatenates a vector of strings.

str_c(x, collapse = ", ")

## [1] "why, video, cross, extra, deal, authority"

str_sub(string, start = 1L, end = -1L) extracts a substring by a position. Positions can be also negative.

str_sub(x, 1, 2)

## [1] "wh" "vi" "cr" "ex" "de" "au"

str_sub(x, -2)

## [1] "hy" "eo" "ss" "ra" "al" "ty"

str_detect(string, pattern, negate = FALSE) tells you if there’s any match to the pattern. Handy in dplyr::filter().

str_detect(x, "[aeiou]")

## [1] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE

str_detect(x, "[aeiou]", negate = TRUE)

## [1]  TRUE FALSE FALSE FALSE FALSE FALSE

str_count(x, pattern) counts the number of patterns.

str_count(x, "[aeiou]")

## [1] 0 3 1 2 2 4

str_subset(string, pattern, negate = FALSE) extracts the matching components. str_which(string, pattern, negate = FALSE) gives indices of the matching components.

str_subset(x, "[aeiou]")

## [1] "video"     "cross"     "extra"     "deal"      "authority"

str_subset(x, "[aeiou]", negate = TRUE)

## [1] "why"

str_which(x, "[aeiou]")

## [1] 2 3 4 5 6

str_locate(string, pattern) gives the position of the match.

str_locate(x, "[aeiou]")

##      start end
## [1,]    NA  NA
## [2,]     2   2
## [3,]     3   3
## [4,]     1   1
## [5,]     2   2
## [6,]     1   1

str_extract(string, pattern) extracts the text of the match. Return value = vector.

## [1] "why"       "video"     "cross"     "extra"     "deal"      "authority"

str_extract(x, "[aeiou]")

## [1] NA  "i" "o" "e" "e" "a"

str_match(string, pattern) extracts parts of the match defined by parentheses (capturing groups). Return value = matrix, with rows being input vector values, and columns being capturing groups. First column represents the full match.

## [1] "why"       "video"     "cross"     "extra"     "deal"      "authority"

str_match(x, "(.)[aeiou](.)")

##      [,1]  [,2] [,3]
## [1,] NA    NA   NA  
## [2,] "vid" "v"  "d" 
## [3,] "ros" "r"  "s" 
## [4,] NA    NA   NA  
## [5,] "dea" "d"  "a" 
## [6,] "aut" "a"  "t"

str_replace(string, pattern, replacement) replaces the matches with new text. replacement also supports references of the form \1, \2, etc., which will be replaced with the contents of the respective matched group (created by ()). str_remove(string, pattern) is an alias for str_replace(string, pattern, "").

str_replace(x, "[aeiou]", "?")

## [1] "why"       "v?deo"     "cr?ss"     "?xtra"     "d?al"      "?uthority"

str_replace_all(x, "[aeiou]", "?")

## [1] "why"       "v?d??"     "cr?ss"     "?xtr?"     "d??l"      "??th?r?ty"

str_replace(x, "(a)", "\\1_")

## [1] "why"        "video"      "cross"      "extra_"     "dea_l"      "a_uthority"

str_remove(x, "[aeiou]")

## [1] "why"      "vdeo"     "crss"     "xtra"     "dal"      "uthority"

str_remove_all(x, "[aeiou]")

## [1] "why"   "vd"    "crss"  "xtr"   "dl"    "thrty"

str_split(string, pattern, n = Inf, simplify = FALSE) splits up a string into multiple pieces.

str_split(c("a,b", "c,d,e"), ",")

## [[1]]
## [1] "a" "b"
## 
## [[2]]
## [1] "c" "d" "e"

`glue` - string interpolation

glue offers the same functionality, which you already know from e.g. Bash ("$variable") or Python (f"{variable}").

library(glue)
name <- "Fred"
age <- 50
anniversary <- as.Date("1991-10-12")
glue("My name is {name}, my age next year is {age + 1}, my anniversary is {format(anniversary, '%A, %B %d, %Y')}.")

## My name is Fred, my age next year is 51, my anniversary is Saturday, October 12, 1991.

Equivalently, we can break long string to multiple parameters on separate lines and use named arguments to assign temporary variables:

glue(
  "My name is {name},",
  " my age next year is {age + 1},",
  " my anniversary is {format(anniversary, '%A, %B %d, %Y')}.",
  name = "Joe",
  age = 40,
  anniversary = as.Date("2001-10-12")
)

`ggplot2` - plotting made easy

ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

To create a plot, you start with initialization of a basic ggplot2 object holding data, and then you add a geom, which tells how to visualize the data and how to map variables to aesthetics (color, size, etc.). This will provide you sensible defaults. To further modify the plot, you can add more components with + operator.

Useful links

Cheatsheet
Tutorial
Quick reference - also colors, point shapes, line types, etc.
Aesthetic specifications - types of shapes, lines, etc.
ggplot2 essentials - there is probably answer to any “How to…?” question.
Top 50 ggplot2 visualizations

ggplot2 is a very extensive library, and so you should mainly know its basic principles and search for details in the reference. We will just look at these basic principles.

library(ggplot2)
mtcars_sm

ggplot(mtcars) +
  geom_point(aes(x = cyl, y = mpg))

We have initialized the ggplot2 object with mtcars data and added geom_point(), which represents a visualization of points in coordinate system. In this geom, we mapped x to cyl and y to mpg variable, respectively.

Each geom_*() has available aesthetics to which you can map variables. Let’s add some colors to the previous plot:

ggplot(mtcars) +
  geom_point(aes(x = cyl, y = mpg, color = gear))

Hmmm but why is gear considered as a continuous variable?

head(mtcars$gear)

## [1] 4 4 4 3 3 3

class(mtcars$gear)

## [1] "numeric"

Because it is defined so.

ggplot2 has very sensible defaults, so, for example, in case of colors it uses continuous or discrete scale, based on variable type. Let’s see what happens when we change gear type to factor:

mtcars2 <- dplyr::mutate(mtcars, gear = factor(gear))
head(mtcars2$gear)

## [1] 4 4 4 3 3 3
## Levels: 3 4 5

ggplot(mtcars2) +
  geom_point(aes(x = cyl, y = mpg, color = gear))

Now you can see that ggplot2 has created the discrete scale, where each level of gear variable has its own color.

Because ggplot2 is a part of tidyverse, it also uses the non-standard evaluation, and so this is equivalent to the example above, but without modifying the mtcars object:

ggplot(mtcars) +
  geom_point(aes(x = cyl, y = mpg, color = factor(gear)))

Aesthetics are inherited and overwritten by last definition. In this way you haven’t to repeat common aesthetics for each geom. This is equivalent to the example above:

ggplot(mtcars, aes(x = cyl, y = mpg, color = factor(gear))) +
  geom_point()

Let’s introduce more ggplot2 components:

We map hp variable to point size in geom_point().
We add one more geom, geom_text(), which is similar to geom_point(), but shows text instead of points.
scale_color_manual() translates discrete values (factor levels) to colors. In general, scales control the details of how data values are translated to visual properties.
labs() adds labels to aesthetics and main plot areas (e.g. title).
theme_bw() is a predefined theme with black and white colors.
theme() customizes the non-data components of your plots. We can override settings in predefined theme_*() themes.

p <- ggplot(mtcars, aes(x = qsec, y = mpg)) +
  geom_point(aes(color = factor(gear), size = hp)) +
  geom_text(aes(label = rownames(mtcars)), color = "black") +
  scale_color_manual(values = c("3" = "orchid4", "4" = "darkcyan", "5" = "darkorange1")) +
  labs(
    title = "mtcars", subtitle = "Acceleration vs. Fuel consumption",
    x = "1/4 mile time", y = "Miles per gallon", size = "Horse Power", color = "Gear"
  ) +
  theme_bw() +
  theme(
    plot.title = element_text(color = "chocolate4", face = "bold"),
    plot.subtitle = element_text(color = "orangered3"),
    axis.title = element_text(face = "bold")
  )

print(p)

Another useful ability of ggplot2 is facetting. facet_wrap() splits a plot to panels according to discrete variable, which is specified by formula. We split the plot by am variable (transmission: 0 = automatic, 1 = manual) and define a translation of labels for its levels.

mtcars$am

##  [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1

p + facet_wrap(~ am, labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")))

We can specify multiple variables and plot is then created for every combination of their levels.

p + facet_wrap(~ am + cyl, labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")))

Similar function is facet_grid() which creates a matrix of plots. Rows and columns are specified by LHS and RHS of formula.

p + facet_grid(am ~ cyl, labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")))

Again, multiple variables are supported, for both LHS and RHS.

p + facet_grid(am + vs ~ cyl, labeller = labeller(am = c("0" = "Automatic", "1" = "Manual")))

Saving is done via ggsave() function. You can use any format R can handle.

ggsave("ggplot2_example.png", p)
ggsave("ggplot2_example.pdf", p)

ggplot2_example.png

ggplot2_example.pdf

Alternatively, you can use base R functions. This is useful for saving multiple plots to a single multipage PDF.

pdf("ggplot2_example.pdf")
print(p)
dev.off()

Using dynamic variable names in aesthetics

You probably wondered, how can you use dynamic variable names in aes(). For example, consider a function plot_pca(data, color_by) which calculates PCA and shows scatterplot colorized by a specified variable. Although it is possible via NSE (see vignette("programming")), the easiest way is to use aes_string() function.

color_by <- "cyl"

ggplot(mtcars) +
  geom_point(aes_string(x = "mpg", y = "qsec", color = color_by))

The disadvantage is that you cannot use the capabilities of NSE. In the example above, you cannot specify factor(color_by) to treat cyl as factor variable.

Libraries extending the `ggplot2`

`ggpubr` - publication-ready plots

ggpubr::ggboxplot(
  mtcars, x = "cyl", y = "qsec",
  color = "cyl", shape = "cyl",
  add = "jitter"
)

`GGally` - useful geoms

GGally::ggpairs(mtcars, columns = c("cyl", "mpg", "qsec"))

`cowplot` - aligning multiple plots

cowplot can do more than aligning plots. For example, it can draw images.

p1 <- ggplot(mtcars) + 
  geom_point(aes(x = disp, y = mpg))
p2 <- ggplot(mtcars) + 
  geom_point(aes(x = disp, y = hp))

cowplot::plot_grid(p1, p2, align = "h")

`ggforce` - cool functions

ggplot(mtcars) +
  geom_point(aes(x = hp, y = qsec, color = factor(gear))) +
  ggforce::facet_zoom(x = gear == 4) +
  theme_bw()

`ggcorrplot` - visualize a correlation matrix

corr <- round(cor(mtcars), 1)
corr[1:5, 1:5]

##       mpg  cyl disp   hp drat
## mpg   1.0 -0.9 -0.8 -0.8  0.7
## cyl  -0.9  1.0  0.9  0.8 -0.7
## disp -0.8  0.9  1.0  0.8 -0.7
## hp   -0.8  0.8  0.8  1.0 -0.4
## drat  0.7 -0.7 -0.7 -0.4  1.0

p_mat <- ggcorrplot::cor_pmat(mtcars)
p_mat[1:5, 1:5]

##               mpg          cyl         disp           hp         drat
## mpg  0.000000e+00 6.112687e-10 9.380327e-10 1.787835e-07 1.776240e-05
## cyl  6.112687e-10 0.000000e+00 1.802838e-12 3.477861e-09 8.244636e-06
## disp 9.380327e-10 1.802838e-12 0.000000e+00 7.142679e-08 5.282022e-06
## hp   1.787835e-07 3.477861e-09 7.142679e-08 0.000000e+00 9.988772e-03
## drat 1.776240e-05 8.244636e-06 5.282022e-06 9.988772e-03 0.000000e+00

p_corr <- ggcorrplot::ggcorrplot(corr, method = "circle", p.mat = p_mat)
print(p_corr)

`ggrepel` - repel overlapping text labels

ggplot(mtcars2, aes(x = qsec, y = mpg)) +
  geom_point(aes(color = factor(gear), size = hp)) +
  ggrepel::geom_text_repel(aes(label = rownames(mtcars)), color = "black")

Additional themes

`ggsci` - a collection of color palettes inspired by (mainly) scientific journals

Nature Publishing Group color palette:

p1 <- ggplot(mtcars) +
  geom_point(aes(x = hp, y = qsec, color = factor(cyl))) +
  theme_bw() +
  ggsci::scale_color_npg()

p2 <- ggplot(mtcars) +
  geom_histogram(aes(x = qsec, fill = factor(cyl)), colour = "black", binwidth = 1, position = "dodge") +
  theme_bw() +
  ggsci::scale_fill_npg()

cowplot::plot_grid(p1, p2, align = "h")

`hrbrthemes` - themes and color palettes

ggplot(mtcars) +
  geom_point(aes(x = hp, y = qsec, color = factor(cyl))) +
  hrbrthemes::theme_modern_rc()

`see` - themes and color palettes

ggplot(mtcars) +
  geom_point(aes(x = hp, y = qsec, color = factor(cyl))) +
  see::theme_abyss()

`cowplot`

Not really a collection of e.g. colors, but useful visual helpers with explanation.

Other useful libraries

`janitor` - table summaries (RIP `table()`)

Personally, I am mainly using these functions:

tabyl() (vignette) creates a frequency table. It’s a replacement for base R table(), which has a weird return value with horrible API.

janitor::tabyl(mtcars, cyl)

janitor::tabyl(mtcars, cyl, gear)

janitor::tabyl(mtcars, cyl, gear, am)

## $`0`
##  cyl  3 4 5
##    4  1 2 0
##    6  2 2 0
##    8 12 0 0
## 
## $`1`
##  cyl 3 4 5
##    4 0 6 2
##    6 0 2 1
##    8 0 0 2

adorn_*() append row and/or column with summary statistics.

janitor::tabyl(mtcars, cyl, gear) %>%
  janitor::adorn_totals(c("row", "col")) %>%
  janitor::adorn_percentages("row") %>%
  janitor::adorn_pct_formatting(rounding = "half up", digits = 0) %>%
  janitor::adorn_ns() %>%
  as.data.frame()

get_dupes() returns duplicated rows.

janitor::get_dupes(mtcars, mpg, hp)

`plotly` - interactive HTML plots

plotly is a JavaScript library with wrappers for R and Python. It is somewhat similar to ggplot2 in terms of The Grammar of Graphics. Output is a HTML file or in case of RMarkdown, it can be directly embedded as chunk output.

We want go into details, so if you are interested, see examples. We will try to replicate the mtcars scatterplot done in ggplot2:

p_ggplot2 <- ggplot(mtcars2, aes(x = qsec, y = mpg)) +
  geom_point(aes(color = factor(gear), size = hp)) +
  theme_bw()

print(p_ggplot2)

p_plotly <- plotly::plot_ly(
  data = mtcars,
  x = ~qsec, y = ~mpg, color = ~factor(gear), size = ~hp,
  type = "scatter", mode = "markers",
  text = ~glue("mpg: {mpg}<br>qsec: {qsec}<br>gear: {gear}<br>hp: {hp}")
)

p_plotly

plotly can also convert ggplot2 objects, but it’s not always accurate.

plotly::ggplotly(p_ggplot2)

But this seems to be quite good, even better than p_plotly.

plotly produces objects inheriting from htmlwidget class. We can use htmlwidgets package to save plotly object to HTML file.

class(p_plotly)

## [1] "plotly"     "htmlwidget"

htmlwidgets::saveWidget(p_plotly, "p_plotly.html")

p_plotly.html

`heatmaply` - interactive HTML heatmaps

heatmaply uses plotly as a backend. Its API is mostly compatible with the classic heatmap.2() function.

heatmaply::heatmaply(mtcars)

It has also functionality similar to ggcorrplot:

cor <- psych::corr.test(mtcars)

heatmaply::heatmaply_cor(
  cor$r,
  node_type = "scatter",
  point_size_mat = -log10(cor$p), 
  point_size_name = "-log10(p-value)",
  label_names = c("x", "y", "Correlation")
)

`pheatmap` - pretty heatmaps in base R

The ordinary heatmap() function in R has several drawbacks when it comes to producing publication quality heatmaps. It is hard to produce pictures with consistent text, cell and overall sizes and shapes. The function pheatmap() tries to alleviate the problems by offering more fine grained control over heatmap dimensions and appearance.

You can find a bioinformatic use case here.

annotation_col <- mtcars[, c("cyl", "gear")]
head(annotation_col)

hclust_cols <- hclust(dist(t(mtcars)), method = "complete") %>%
  dendextend::cutree(tree = as.dendrogram(.), k = 2)
annotation_row <- data.frame(
  Cluster = if_else(hclust_cols == 1, "Cluster 1", "Cluster 2"),
  row.names = colnames(mtcars)
)
head(annotation_row)

pheatmap::pheatmap(t(mtcars), annotation_col = annotation_col, annotation_row = annotation_row)

Parallelization

BiocParallel package provides easy-to-use functions for parallel execution. We won’t go into details and just introduce probably the most used function bplapply(), a parallelized version of lapply().

As you know, lapply(X, FUN, ...) applies FUN with optional arguments ... to each element of X:

x <- 1:5
fn <- function(i) i^2
lapply(x, fn)

## [[1]]
## [1] 1
## 
## [[2]]
## [1] 4
## 
## [[3]]
## [1] 9
## 
## [[4]]
## [1] 16
## 
## [[5]]
## [1] 25

bplapply(X, FUN, ..., BPREDO = list(), BPPARAM = bpparam()) does the same, but each element is processed in parallel. Result is guaranteed to have the same order of elements as in X.

BiocParallel::bplapply(x, fn)

## [[1]]
## [1] 1
## 
## [[2]]
## [1] 4
## 
## [[3]]
## [1] 9
## 
## [[4]]
## [1] 16
## 
## [[5]]
## [1] 25

Defaultly, all available cores are used. To control for resources, use the BPPARAM argument.

(bpparam <- BiocParallel::MulticoreParam(workers = 2))

## class: MulticoreParam
##   bpisup: FALSE; bpnworkers: 2; bptasks: 0; bpjobname: BPJOB
##   bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
##   bpRNGseed: ; bptimeout: 2592000; bpprogressbar: FALSE
##   bpexportglobals: TRUE
##   bplogdir: NA
##   bpresultdir: NA
##   cluster type: FORK

BiocParallel::bplapply(x, fn, BPPARAM = bpparam)

Parallel processing is hard to debug, even with try-catch equivalent function bptry(). Also, you can’t use browser() inside FUN. But sometimes is just enough to run bplapply() in non-parallel (serial) mode. This is possible with SerialParam().

(bpparam <- BiocParallel::SerialParam())
fn <- function(i) {
  browser()
  i^2
}

BiocParallel::bplapply(x, fn, BPPARAM = bpparam)

BiocParallel supports jobs in distributed systems and much more, but for now we can live only with the simple multicore usage.

Cleanup

save.image("intro_to_R.RData")

warnings()
traceback()

## 2: match.fun(FUN)
## 1: lapply(as.matrix(mtcars))

sessionInfo()

## R version 4.0.4 (2021-02-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
##  [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggplot2_3.3.3    stringr_1.4.0    dplyr_1.0.4      magrittr_2.0.1   emo_0.0.0.9000   rmdformats_1.0.1 here_1.0.1       glue_1.4.2       knitr_1.31      
## [10] rmarkdown_2.7    conflicted_1.0.4
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1         backports_1.2.1      systemfonts_1.0.1    plyr_1.8.6           lazyeval_0.2.2       splines_4.0.4        crosstalk_1.1.1     
##   [8] BiocParallel_1.24.1  TH.data_1.0-10       digest_0.6.27        foreach_1.5.1        htmltools_0.5.1.1    viridis_0.5.1        fansi_0.4.2         
##  [15] memoise_2.0.0        see_0.6.2            openxlsx_4.2.3       extrafont_0.17       sandwich_3.0-0       extrafontdb_1.0      svglite_2.0.0       
##  [22] colorspace_2.0-0     blob_1.2.1           rvest_0.3.6          ggrepel_0.9.1        haven_2.3.1          xfun_0.21            crayon_1.4.1        
##  [29] jsonlite_1.7.2       survival_3.2-7       zoo_1.8-8            iterators_1.0.13     kableExtra_1.3.4     polyclip_1.10-0      registry_0.5-1      
##  [36] gtable_0.3.0         emmeans_1.5.4        webshot_0.5.2        car_3.0-10           Rttf2pt1_1.3.8       BiocGenerics_0.36.0  abind_1.4-5         
##  [43] scales_1.1.1         pheatmap_1.0.12      mvtnorm_1.1-1        DBI_1.1.1            GGally_2.1.0         rstatix_0.7.0        Rcpp_1.0.6          
##  [50] viridisLite_0.3.0    xtable_1.8-4         tmvnsim_1.0-2        bit_4.0.4            foreign_0.8-81       stats4_4.0.4         DT_0.17             
##  [57] htmlwidgets_1.5.3    httr_1.4.2           RColorBrewer_1.1-2   ellipsis_0.3.1       pkgconfig_2.0.3      reshape_0.8.8        farver_2.0.3        
##  [64] sass_0.3.1           utf8_1.1.4           janitor_2.1.0        reshape2_1.4.4       labeling_0.4.2       AnnotationDbi_1.52.0 tidyselect_1.1.0    
##  [71] rlang_0.4.10         ggcorrplot_0.1.3     effectsize_0.4.3     munsell_0.5.0        cellranger_1.1.0     tools_4.0.4          cachem_1.0.4        
##  [78] cli_2.3.1            RSQLite_2.2.3        generics_0.1.0       broom_0.7.5          ggridges_0.5.3       evaluate_0.14        fastmap_1.1.0       
##  [85] heatmaply_1.2.1      yaml_2.2.1           bit64_4.0.5          zip_2.1.1            purrr_0.3.4          dendextend_1.14.0    nlme_3.1-152        
##  [92] xml2_1.3.2           debugme_1.1.0        compiler_4.0.4       rstudioapi_0.13      plotly_4.9.3         curl_4.3             ggsignif_0.6.1      
##  [99] tibble_3.1.0         tweenr_1.0.1         bslib_0.2.4          stringi_1.5.3        highr_0.8            parameters_0.12.0    forcats_0.5.1       
## [106] gdtools_0.2.3        hrbrthemes_0.8.0     lattice_0.20-41      Matrix_1.3-2         psych_2.0.12         ggsci_2.9            vctrs_0.3.6         
## [113] pillar_1.5.0         lifecycle_1.0.0      jquerylib_0.1.3      estimability_1.3     data.table_1.14.0    cowplot_1.1.1        insight_0.13.1      
## [120] seriation_1.2-9      R6_2.5.0             bookdown_0.21        TSP_1.1-10           gridExtra_2.3        rio_0.5.16           IRanges_2.24.1      
## [127] codetools_0.2-18     MASS_7.3-53.1        assertthat_0.2.1     rprojroot_2.0.2      withr_2.4.1          mnormt_2.0.2         S4Vectors_0.28.1    
## [134] multcomp_1.4-16      bayestestR_0.8.2     parallel_4.0.4       hms_1.0.0            grid_4.0.4           tidyr_1.1.2          coda_0.19-4         
## [141] snakecase_0.11.0     carData_3.0-4        ggpubr_0.4.0         ggforce_0.3.2        Biobase_2.50.0       lubridate_1.7.9.2

HTML rendering

This chunk is not evaluated (eval = FALSE). Otherwise you will probably end up in recursive hell 🤯

library(conflicted)
library(rmarkdown)
library(knitr)
library(glue)
library(here)

if (!require(rmdformats)) {
  BiocManager::install("rmdformats")
}

# You can set global chunk options. Options set in individual chunks will override this.
opts_chunk$set(warning = FALSE, message = FALSE)
render(here("E02-intro_to_R/intro_to_R.Rmd"), output_file = here("E02-intro_to_R/intro_to_R.html"))

E02 - Intro to R (Analysis of Gene Expression @ VSCHT)